docs: def sg_accuracy(tensor, opt):
      r"""Returns accuracy of predictions.
      
      Args:
        tensor: A `Tensor`. Probability distributions or unscaled prediction scores.
        opt:
          target: A 'Tensor`. Labels.
        
      Returns:
        A `Tensor` of the same shape as `tensor`. Each value will be 1 if correct else 0. 
      
      For example,
      
      ```
      tensor = [[20.1, 18, -4.2], [0.04, 21.1, 31.3]]
      target = [[0, 1]]
      tensor.sg_accuracy(target=target) => [[ 1.  0.]]
      ```
      """
      assert opt.target is not None, 'target is mandatory.'
      opt += tf.sg_opt(k=1)
  
      # # calc accuracy
      out = tf.identity(tf.equal(tensor.sg_argmax(), tf.cast(opt.target, tf.int64)).sg_float(), name='acc')
      # out = tf.identity(tf.nn.in_top_k(tensor, opt.target, opt.k).sg_float(), name='acc')
  
      return out